Distributed Sequencial Gaussian Simulation

ABSTRACT

A method for processing a well data log may comprise adding one or more boundary areas to the well data log, dividing the well data log into one or more segments using the one or more boundary areas, processing each of the one or more segments on one or more information handling systems, and reforming each of the one or more segments into a final simulation. A system for processing a well data log may comprise one or more information handling systems in a cluster. The one or more information handling systems may be configured to perform the method for processing the well data log.

BACKGROUND

Boreholes drilled into subterranean formations may enable recovery ofdesirable fluids (e.g., hydrocarbons) using a number of differenttechniques. A downhole tool may be employed in subterranean operationsto determine borehole and/or formation properties. During theseoperations, measurements from the downhole tool may be formed into welldata logs. These well data logs may further be turned into simulations.

The well data logs provide the petrophysical properties such as theporosity values only at the well locations. In order to estimate thepetrophysical properties at the entire volume, some stochasticsimulation algorithms such as Sequential Gaussian Simulation (SGS) areapplied. The traditional SGS first generates a random path covering theentire volume. At each location of the path, it uses the well data logand the previous simulated data nearby the location to estimate aconditional Gaussian distribution, and then draws a value from thatdistribution for that location, i.e. simulating, and repeat the processuntil all locations in the random path get a simulated value. Since thetraditional SGS runs only on single machines, it does not scale tosupport large models due to the limitation of the computer's memory(RAM) and CPUs.

BRIEF DESCRIPTION OF THE DRAWINGS

These drawings illustrate certain aspects of some examples of thepresent disclosure and should not be used to limit or define thedisclosure.

FIG. 1 is a schematic view of an information handling system;

FIG. 2 is another schematic view of the information handling system;

FIGS. 3A-3E illustrate a method for banding during sequential gaussiansimulations;

FIG. 4 is an example of a neural network; and

FIG. 5 is a workflow for performing a sequential gaussian simulation andparallelizing the simulation.

DETAILED DESCRIPTION

The present disclosure relates generally to a system and method forperforming a sequential gaussian simulation. Specifically, using SGS andSIS simulation techniques with parallelizing techniques. This reducedprocessing time from hours to minutes, which enable better uncertaintyanalysis. Sequential Gaussian Simulation (SGS) is one of most popularGeostatistics algorithms to simulate petrophysical properties such asporosity and permeability. The algorithm uses the values at the previoussimulated locations to simulate the value at the next locationsequentially. When those locations are distributed into differentmachines in the distributed computing, although it is possible toretrieve the values at those locations for next location, the process isextremely slow for a simulation with millions of locations to besimulated. The traditional SGS runs in a single machine. It does notprovide scalability. As disclosure below, the methods and systems modifythe traditional SGS to run in cloud computing or other distributedenvironments with scalability, and without sacrificing the performancedramatically. The scalability of the proposed approach enables thesupport of large models up to billions of locations, and many simulatedrealizations/models which would provide better statistics about thedistribution of the petrophysical properties, hence the improvement ofuncertainty analysis.

Sequential gaussian simulations are performed on well data logs. Welldata logs are populated from different methods and systems. Two types ofwell measurement systems are systems disposed on a conveyance forlogging and measurements or on a bottom hole assembly during drillingoperations. The bottom hole assembly may be a measurement-while drilling(MWD) or logging-while-drilling (LWD) system. In either case,measurements and data may be taken and used to populate a well data log.The well data logs may then be computed on an information handlingsystem using sequential gaussian simulations.

FIG. 1 illustrates an example information handling system 100 which maybe employed to perform various steps, methods, and techniques disclosedherein. Persons of ordinary skill in the art will readily appreciatethat other system examples are possible. As illustrated, informationhandling system 100 includes a processing unit (CPU or processor) 102and a system bus 104 that couples various system components includingsystem memory 106 such as read only memory (ROM) 108 and random accessmemory (RAM) 210 to processor 102. Processors disclosed herein may allbe forms of this processor 102. Information handling system 100 mayinclude a cache 112 of high-speed memory connected directly with, inclose proximity to, or integrated as part of processor 102. Informationhandling system 100 copies data from memory 106 and/or storage device114 to cache 112 for quick access by processor 102. In this way, cache112 provides a performance boost that avoids processor 102 delays whilewaiting for data. These and other modules may control or be configuredto control processor 102 to perform various operations or actions. Othersystem memory 106 may be available for use as well. Memory 106 mayinclude multiple different types of memory with different performancecharacteristics. It may be appreciated that the disclosure may operateon information handling system 100 with more than one processor 102 oron a group or cluster of computing devices networked together to providegreater processing capability. Processor 102 may include any generalpurpose processor and a hardware module or software module, such asfirst module 116, second module 118, and third module 120 stored instorage device 114, configured to control processor 102 as well as aspecial-purpose processor where software instructions are incorporatedinto processor 102. Processor 102 may be a self-contained computingsystem, containing multiple cores or processors, a bus, memorycontroller, cache, etc. A multi-core processor may be symmetric orasymmetric. Processor 102 may include multiple processors, such as asystem having multiple, physically separate processors in differentsockets, or a system having multiple processor cores on a singlephysical chip. Similarly, processor 102 may include multiple distributedprocessors located in multiple separate computing devices, but workingtogether such as via a communications network. Multiple processors orprocessor cores may share resources such as memory 106 or cache 112, ormay operate using independent resources. Processor 102 may include oneor more state machines, an application specific integrated circuit(ASIC), or a programmable gate array (PGA) including a field PGA (FPGA).

Each individual component discussed above may be coupled to system bus104, which may connect each and every individual component to eachother. System bus 104 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 108 or the like, may provide the basicroutine that helps to transfer information between elements withininformation handling system 100, such as during start-up. Informationhandling system 100 further includes storage devices 114 orcomputer-readable storage media such as a hard disk drive, a magneticdisk drive, an optical disk drive, tape drive, solid-state drive, RAMdrive, removable storage devices, a redundant array of inexpensive disks(RAID), hybrid storage device, or the like. Storage device 114 mayinclude software modules 116, 118, and 120 for controlling processor102. Information handling system 100 may include other hardware orsoftware modules. Storage device 114 is connected to the system bus 104by a drive interface. The drives and the associated computer-readablestorage devices provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data forinformation handling system 100. In one aspect, a hardware module thatperforms a particular function includes the software component stored ina tangible computer-readable storage device in connection with thenecessary hardware components, such as processor 102, system bus 104,and so forth, to carry out a particular function. In another aspect, thesystem may use a processor and computer-readable storage device to storeinstructions which, when executed by the processor, cause the processorto perform operations, a method, or other specific actions. The basiccomponents and appropriate variations may be modified depending on thetype of device, such as whether information handling system 100 is asmall, handheld computing device, a desktop computer, or a computerserver. When processor 102 executes instructions to perform“operations”, processor 102 may perform the operations directly and/orfacilitate, direct, or cooperate with another device or component toperform the operations.

As illustrated, information handling system 100 employs storage device114, which may be a hard disk or other types of computer-readablestorage devices which may store data that are accessible by a computer,such as magnetic cassettes, flash memory cards, digital versatile disks(DVDs), cartridges, random access memories (RAMs) 110, read only memory(ROM) 108, a cable containing a bit stream and the like, may also beused in the exemplary operating environment. Tangible computer-readablestorage media, computer-readable storage devices, or computer-readablememory devices, expressly exclude media such as transitory waves,energy, carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with information handling system 100, aninput device 122 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. Additionally,input device 122 may take in data from one or more sensors 136,discussed above. An output device 124 may also be one or more of anumber of output mechanisms known to those of skill in the art. In someinstances, multimodal systems enable a user to provide multiple types ofinput to communicate with information handling system 100.Communications interface 126 generally governs and manages the userinput and system output. There is no restriction on operating on anyparticular hardware arrangement and therefore the basic hardwaredepicted may easily be substituted for improved hardware or firmwarearrangements as they are developed.

As illustrated, each individual component describe above is depicted anddisclosed as individual functional blocks. The functions these blocksrepresent may be provided through the use of either shared or dedicatedhardware, including, but not limited to, hardware capable of executingsoftware and hardware, such as a processor 102, that is purpose-built tooperate as an equivalent to software executing on a general purposeprocessor. For example, the functions of one or more processorspresented in FIG. 1 may be provided by a single shared processor ormultiple processors. (Use of the term “processor” should not beconstrued to refer exclusively to hardware capable of executingsoftware.) Illustrative embodiments may include microprocessor and/ordigital signal processor (DSP) hardware, read-only memory (ROM) 108 forstoring software performing the operations described below, and randomaccess memory (RAM) 110 for storing results. Very large scaleintegration (VLSI) hardware embodiments, as well as custom VLSIcircuitry in combination with a general purpose DSP circuit, may also beprovided.

The logical operations of the various methods, described below, areimplemented as: (1) a sequence of computer implemented steps,operations, or procedures running on a programmable circuit within ageneral use computer, (2) a sequence of computer implemented steps,operations, or procedures running on a specific-use programmablecircuit; and/or (3) interconnected machine modules or program engineswithin the programmable circuits. Information handling system 100 maypractice all or part of the recited methods, may be a part of therecited systems, and/or may operate according to instructions in therecited tangible computer-readable storage devices. Such logicaloperations may be implemented as modules configured to control processor102 to perform particular functions according to the programming ofsoftware modules 116, 118, and 120.

In examples, one or more parts of the example information handlingsystem 100, up to and including the entire information handling system100, may be virtualized. For example, a virtual processor may be asoftware object that executes according to a particular instruction set,even when a physical processor of the same type as the virtual processoris unavailable. A virtualization layer or a virtual “host” may enablevirtualized components of one or more different computing devices ordevice types by translating virtualized operations to actual operations.Ultimately however, virtualized hardware of every type is implemented orexecuted by some underlying physical hardware. Thus, a virtualizationcompute layer may operate on top of a physical compute layer. Thevirtualization compute layer may include one or more virtual machines,an overlay network, a hypervisor, virtual switching, and any othervirtualization application

FIG. 2 illustrates an example information handling system 100 having achipset architecture that may be used in executing the described methodand generating and displaying a graphical user interface (GUI).Information handling system 100 is an example of computer hardware,software, and firmware that may be used to implement the disclosedtechnology. Information handling system 100 may include a processor 102,representative of any number of physically and/or logically distinctresources capable of executing software, firmware, and hardwareconfigured to perform identified computations. Processor 102 maycommunicate with a chipset 100 that may control input to and output fromprocessor 102. In this example, chipset 200 outputs information tooutput device 124, such as a display, and may read and write informationto storage device 114, which may include, for example, magnetic media,and solid state media. Chipset 200 may also read data from and writedata to RAM 110. A bridge 202 for interfacing with a variety of userinterface components 204 may be provided for interfacing with chipset200. Such user interface components 204 may include a keyboard, amicrophone, touch detection and processing circuitry, a pointing device,such as a mouse, and so on. In general, inputs to information handlingsystem 100 may come from any of a variety of sources, machine generatedand/or human generated.

Chipset 200 may also interface with one or more communication interfaces126 that may have different physical interfaces. Such communicationinterfaces may include interfaces for wired and wireless local areanetworks, for broadband wireless networks, as well as personal areanetworks. Some applications of the methods for generating, displaying,and using the GUI disclosed herein may include receiving ordereddatasets over the physical interface or be generated by the machineitself by processor 102 analyzing data stored in storage device 114 orRAM 110. Further, information handling system 100 receive inputs from auser via user interface components 404 and execute appropriatefunctions, such as browsing functions by interpreting these inputs usingprocessor 102.

In examples, information handling system 100 may also include tangibleand/or non-transitory computer-readable storage devices for carrying orhaving computer-executable instructions or data structures storedthereon. Such tangible computer-readable storage devices may be anyavailable device that may be accessed by a general purpose or specialpurpose computer, including the functional design of any special purposeprocessor as described above. By way of example, and not limitation,such tangible computer-readable devices may include RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other device which may be used to carryor store desired program code in the form of computer-executableinstructions, data structures, or processor chip design. Wheninformation or instructions are provided via a network, or anothercommunications connection (either hardwired, wireless, or combinationthereof), to a computer, the computer properly views the connection as acomputer-readable medium. Thus, any such connection is properly termed acomputer-readable medium. Combinations of the above should also beincluded within the scope of the computer-readable storage devices.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

In additional examples, methods may be practiced in network computingenvironments with many types of computer system configurations,including personal computers, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, and the like. Examplesmay also be practiced in distributed computing environments where tasksare performed by local and remote processing devices that are linked(either by hardwired links, wireless links, or by a combination thereof)through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices

Information handling system 100 (e.g., referring to FIGS. 1 and 2) mayfurther take measurements taken by downhole tool 102 during loggingoperations through a plurality of sensors 128 (e.g., referring to FIGS.1 and 2) to produce a well log. In examples, the well log may be basedon depth and properties of formation 132 (e.g., referring to FIGS. 1 and2). The well log may include a continuous measurement of formationproperties with depth resolutions, which may also be identified as awell data log. In a well log, well data log may exhibit variation alongdesignated measured sections, i.e., a chosen depth or set of depths tobe investigated, due to heterogeneous nature of rocks within formation132. Variations are attributed to formation 132 being comprised ofdifferent types of rock and that rock being saturated with varyingamounts of water, oil, and gas depending on the depth of the formationbeing examined. The depth dependence of fluid content is a function offluid density. Additionally, formations 132 may include faults and otherpetrophysical baffles that vary azimuthally. The well data log may beused to create a simulation. The well data logs provide thepetrophysical properties such as the porosity values only at the welllocations. In order to estimate the petrophysical properties at theentire volume, some stochastic simulation algorithms such as SequentialGaussian Simulation (SGS) are applied. The traditional SGS firstgenerates a random path covering the entire volume. At each location ofthe path, it uses the well data log and the previous simulated datanearby the location to estimate a conditional Gaussian distribution, andthen draws a value from that distribution for that location, i.e.simulating, and repeat the process until all locations in the randompath get a simulated value. Since the traditional SGS runs only onsingle machines, it does not scale to support large models due to thelimitation of the computer's memory (RAM) and CPUs. The proposed methoddescribed below, may allow the SGS running on many computerssimultaneously. By dividing the simulation into many computersinnovatively—that is, dividing the entire volume into many smallervolumes, distributes those smaller volumes into many computers in thecloud, and run the SGS on those computers. In such as a way, an originallarge model with billions of locations now becomes a collection of manysmaller models with possibly only millions of locations on differentcomputers. And the simulation can be run on those computerssimultaneously. Hence, it becomes possible to support large models up tobillions of locations without sacrificing the performance and accuracy.]

FIG. 3A illustrates simulation 300 that may be processed by informationhandling system 100 (e.g., referring to FIG. 1) under a sequentialgaussian simulation. During this processing, sequential gaussiansimulations are difficult to parallelize due to the simulation'ssequential nature. Parallelize is defined as a computation that may beperformed by one or more information handling systems 100 (e.g.,referring to FIG. 1), simultaneously. However, since SGS requires theprevious simulated values in order to estimate the following simulatedvalues, it requires the simulation to be performed one after another.This may make it difficult with a single information handling system 100to compute parallelizing.

The method described below overcomes the problem by generating one ormore overlapping bands and then distributing each segment created by theoverlapping band to different processing nodes. Each node may be anindependent information handling system 100 connected to each other by anetwork either onsite or offsite. Currently, SGS and SequentialIndicator Simulation (SIS) are the oldest simulation techniques beingused to process information. The ability to parallelize SGS and SIS is amajor improvement to current technology. Additionally, the methods isflexible to be scaled, depending on cluster size. A cluster is acollection of managed information handling system 100. The cluster sizemeans how many information handling systems 100 in the collection andhow many total CPUs from those information handling systems 100. Addingthe ability to parallelize SGS and SIS may reduce processing time ofwell log date from hours to minutes.

With continued reference to FIG. 3A, simulation 300 may be banded bydefining one or more boundary areas 302. The size of the boundary area302 may be based at least in part on the variogram parameter, to make itat least the size of the range in each direction. A variogram parameteris a geostatistics tool to measure the dis-continuity of a spatialproperty such as porosity. It may be described by one or more parameterssuch as the type, the continuity of ranges, and the contributions. Arange of a variogram is a distance in which two chosen locations withinthat distance may have any correlation. FIG. 3B illustrates data 304from simulation 300 within boundary area 302. In FIG. 3C, the boundaryareas 302 are divided to form as many segments 306 for availableprocessing resources. For example, if six information handling systems100 are available for processing in a cluster, cloud, network, or thelike, then six segments 306 may be formed. Additionally, the size ofeach segment 306 should be a size that allows for quick processing. Aseach information handling system 100 has limited memory RAM. If the sizeof each segment 306 is too large, it may not fit into the RAM. Thus, thesize of each segment 306 is limited by the size of RAM.

With continued reference to FIG. 3C, each segment may be sent forprocessing with a least at least two parts 308 of boundary area 302.This may allow each segment 306 to be computed to condition thesimulation properly. As discussed above, each segment 306 may be sent toan individual information handling system 100 in a neural network.

FIG. 4 illustrates a neural network 400 that may include one or moreinformation handling systems 100. As illustrated, simulation 300 isdivided into one or more segments 306, which are processed by individualinformation handling systems 100. After processing each segment 306, aprocessed segment 310 is produced from each information handling system100.

Referring to FIG. 3D, FIG. 3D illustrates each processed segment 310 mayinclude simulated values. In FIG. 3E, processed segments 310 arereformed into final simulation 312. Final simulation 312 providessimulated values collected from each of the one or more informationhandling systems 100 (e.g., referring to FIG. 1) in the cluster,network, and/or the like. Processed segments 310 with the simulatedvalues plus the original well data log are sent to the computers in thecluster. Then two kinds of data (simulated values on the segment and thewell data) are used as the control data by SGS to perform the simulationon each information handling systems 100 in the cluster. Aftersimulation on information handling systems 100 is done, the simulatedvalues are combined together to form the final simulation, which iscalled a model. If more than one realization is performed, the processwill generate multiple models. Those models are then used for volumetriccomputation and uncertainty analysis. For example. Those models are usedto estimated how much oil in the volume, and what the expected amount ofoil and what is the standard errors of the estimation.

FIG. 5 is a workflow 500 for performing a sequential gaussian simulationand parallelizing the simulation. Workflow 500 may begin with block 502in which one or more boundary areas are added to a simulation. The sizeof the boundary area may be based at least in part on the variogramparameter, to make it at least the size of the range in each direction.In block 504 the simulation is divided along the boundary areas into oneor more segments to available processing resources. For example, if sixinformation handling systems 100 (e.g., referring to FIG. 1) areavailable for processing, then six segments may be formed. In block 506the one or more segments may be sent for processing. In block 508 eachsegment, after processing in block 506, are reformed into finalsimulation.

Current technology utilizes SGS to generate a random path covering theentire volume. At each location of the path, it uses the well data logand the previous simulated data nearby the location to estimate aconditional Gaussian distribution, and then draws a value from thatdistribution for that location, i.e. simulating, and repeat the processuntil all locations in the random path get a simulated value. Since thetraditional SGS runs only on single machines, it does not scale tosupport large models due to the limitation of the computer's memory(RAM) and CPUs. The methods and systems described above allows the SGSrunning on many computers simultaneously. By dividing the simulationinto many computers innovatively—that is, dividing the entire volumeinto many smaller volumes, distributes those smaller volumes into manycomputers in the cloud, and run the SGS on those computers. In such as away, an original large model with billions of locations now becomes acollection of many smaller models with possibly only millions oflocations on different computers. And the simulation may be run on thosecomputers simultaneously. Hence, it becomes possible to support largemodels up to billions of locations without sacrificing the performanceand accuracy. The systems and methods may include any of the variousfeatures of the systems and methods disclosed herein, including one ormore of the following statements.

Statement 1. A method for processing a well data log may comprise addingone or more boundary areas to the well data log, dividing the well datalog into one or more segments using the one or more boundary areas,processing each of the one or more segments on one or more informationhandling systems, and reforming each of the one or more segments into afinal simulation.

Statement 2. The method of statement 1, wherein the one or more boundaryareas are defined by a variogram parameter.

Statement 3. The method of statement 2, wherein the variogram parameteris bound by a distance in which two chosen locations within the distancehave correlation.

Statement 4. The method of statement 3, wherein the one or more boundaryareas includes data from the well data log.

Statement 5. The method of statements 1 or 2, wherein the one or moreboundary areas are based at least in part on a cluster size.

Statement 6. The method of statements 1, 2, or 5, wherein each of theone or more segments are processed using Sequential Gaussian Simulation(SGS) or Sequential Indicator Simulation (SIS).

Statement 7. The method of statements 1, 2, 5, or 6, wherein the one ormore segments are formed based at least in part on a number ofinformation handling systems in a cluster available for processing.

Statement 8. The method of statement 7, wherein each of the informationhandling systems are a node in the cluster.

Statement 9. The method of statement 8, wherein each of the one or moresegments reduce computational time of the well data log.

Statement 10. The method of statements 1, 2, or 5-7, wherein the finalsimulation forms at least one model that is configured to estimate howmuch oil is in a volume and a standard error for the at least one model.

Statement 11. A system for processing a well data log may comprise oneor more information handling systems in a cluster. The one or moreinformation handling systems may be configured to add one or moreboundary areas to the well data log, divide the well data log into oneor more segments using the one or more boundary areas, process each ofthe one or more segments on the one or more information handlingsystems, and reform each of the one or more segments into a finalsimulation.

Statement 12. The system of statement 11, wherein the one or moreboundary areas are defined by a variogram parameter.

Statement 13. The system of statement 12, wherein the variogramparameter is bound by a distance in which two chosen locations withinthe distance have correlation.

Statement 14. The system of statement 13, wherein the one or moreboundary area includes data from the well data log.

Statement 15. The system of statements 11 or 12, wherein the one or moreboundary area is based at least in part on a cluster size.

Statement 16. The system of statements 11, 12, or 15, wherein each ofthe one or more segments are processed using Sequential GaussianSimulation (SGS) or Sequential Indicator Simulation (SIS).

Statement 17. The system of statements 11, 12, 15, or 16, wherein theone or more segments are formed based at least in part on a number ofinformation handling systems in the cluster available for processing.

Statement 18. The system of statement 17, wherein each of theinformation handling systems are a node in the cluster.

Statement 19. The system of statement 18, wherein each of the one ormore segments reduce computational time of the well data log.

Statement 20. The system of statements 11, 12, or 15-17, wherein thefinal simulation forms at least one model that is configured to estimatehow much oil is in a volume and a standard error for the at least onemodel.

Although the present disclosure and its advantages have been describedin detail, it should be understood that various changes, substitutions,and alterations may be made herein without departing from the spirit andscope of the disclosure as defined by the appended claims. The precedingdescription provides various examples of the systems and methods of usedisclosed herein which may contain different method steps andalternative combinations of components. It should be understood that,although individual examples may be discussed herein, the presentdisclosure covers all combinations of the disclosed examples, including,in examples, the different component combinations, method stepcombinations, and properties of the system. It should be understood thatthe compositions and methods are described in terms of “comprising,”“containing,” or “including” various components or steps, thecompositions and methods can also “consist essentially of” or “consistof” the various components and steps. Moreover, the indefinite articles“a” or “an,” as used in the claims, are defined herein to mean one ormore than one of the elements that it introduces.

For the sake of brevity, only certain ranges are explicitly disclosedherein. However, ranges from any lower limit may be combined with anyupper limit to recite a range not explicitly recited, as well as, rangesfrom any lower limit may be combined with any other lower limit torecite a range not explicitly recited, in the same way, ranges from anyupper limit may be combined with any other upper limit to recite a rangenot explicitly recited. Additionally, whenever a numerical range with alower limit and an upper limit is disclosed, any number and any includedrange falling within the range are specifically disclosed. Inparticular, every range of values (of the form, “from about a to aboutb,” or, equivalently, “from approximately a to b,” or, equivalently,“from approximately a-b”) disclosed herein is to be understood to setforth every number and range encompassed within the broader range ofvalues even if not explicitly recited. Thus, every point or individualvalue may serve as its own lower or upper limit combined with any otherpoint or individual value or any other lower or upper limit, to recite arange not explicitly recited.

Therefore, the present examples are well adapted to attain the ends andadvantages mentioned as well as those that are inherent therein. Theparticular examples disclosed above are illustrative only, and may bemodified and practiced in different but equivalent manners apparent tothose skilled in the art having the benefit of the teachings herein.Although individual examples are discussed, the disclosure covers allcombinations of all of the examples. Furthermore, no limitations areintended to the details of construction or design herein shown, otherthan as described in the claims below. Also, the terms in the claimshave their plain, ordinary meaning unless otherwise explicitly andclearly defined by the patentee. It is therefore evident that theparticular illustrative examples disclosed above may be altered ormodified and all such variations are considered within the scope andspirit of those examples. If there is any conflict in the usages of aword or term in this specification and one or more patent(s) or otherdocuments that may be incorporated herein by reference, the definitionsthat are consistent with this specification should be adopted.

What is claimed is:
 1. A method for processing a well data logcomprising: adding one or more boundary areas to the well data log;dividing the well data log into one or more segments using the one ormore boundary areas; processing each of the one or more segments on oneor more information handling systems; and reforming each of the one ormore segments into a final simulation.
 2. The method of claim 1, whereinthe one or more boundary areas are defined by a variogram parameter. 3.The method of claim 2, wherein the variogram parameter is bound by adistance in which two chosen locations within the distance havecorrelation.
 4. The method of claim 3, wherein the one or more boundaryareas includes data from the well data log.
 5. The method of claim 1,wherein the one or more boundary areas are based at least in part on acluster size.
 6. The method of claim 1, wherein each of the one or moresegments are processed using Sequential Gaussian Simulation (SGS) orSequential Indicator Simulation (SIS).
 7. The method of claim 1, whereinthe one or more segments are formed based at least in part on a numberof information handling systems in a cluster available for processing.8. The method of claim 7, wherein each of the information handlingsystems are a node in the cluster.
 9. The method of claim 8, whereineach of the one or more segments reduce computational time of the welldata log.
 10. The method of claim 1, wherein the final simulation formsat least one model that is configured to estimate how much oil is in avolume and a standard error for the at least one model.
 11. A system forprocessing a well data log comprising: one or more information handlingsystems in a cluster configured to: add one or more boundary areas tothe well data log; divide the well data log into one or more segmentsusing the one or more boundary areas; process each of the one or moresegments on the one or more information handling systems; and reformeach of the one or more segments into a final simulation.
 12. The systemof claim 11, wherein the one or more boundary areas are defined by avariogram parameter.
 13. The system of claim 12, wherein the variogramparameter is bound by a distance in which two chosen locations withinthe distance have correlation.
 14. The system of claim 13, wherein theone or more boundary area includes data from the well data log.
 15. Thesystem of claim 11, wherein the one or more boundary area is based atleast in part on a cluster size.
 16. The system of claim 11, whereineach of the one or more segments are processed using Sequential GaussianSimulation (SGS) or Sequential Indicator Simulation (SIS).
 17. Thesystem of claim 11, wherein the one or more segments are formed based atleast in part on a number of information handling systems in the clusteravailable for processing.
 18. The system of claim 17, wherein each ofthe information handling systems are a node in the cluster.
 19. Thesystem of claim 18, wherein each of the one or more segments reducecomputational time of the well data log.
 20. The system of claim 11,wherein the final simulation forms at least one model that is configuredto estimate how much oil is in a volume and a standard error for the atleast one model.